A Combined Model for Simulating the Spatial Dynamics of Epidemic Spread: Integrating Stochastic Compartmentalization and Cellular Automata Approach
This research focuses on a combined simulation model for analyzing the spatial distribution of epidemics by combining the global mixing assumption of individuals with two-dimensional probabilistic cellular automata (CA). The model presented in this paper is designed to simulate the spatial distribut...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Ram Arti Publishers
2025-04-01
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| Series: | International Journal of Mathematical, Engineering and Management Sciences |
| Subjects: | |
| Online Access: | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/26-IJMEMS-24-0654-10-2-522-536-2025.pdf |
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| Summary: | This research focuses on a combined simulation model for analyzing the spatial distribution of epidemics by combining the global mixing assumption of individuals with two-dimensional probabilistic cellular automata (CA). The model presented in this paper is designed to simulate the spatial distribution of diseases in a spatially structured population. It incorporates a stochastic compartment model that uses the mixing regime, a two-dimensional probabilistic CA for constructing a decision support system for controlling epidemics. The model positions elementary populations in a regular two-dimensional lattice, whereas the compartment model applies to sets of persons who have the same epidemic regime in the community. Previous epidemic models involved dynamic compartment models with global mixing and are incorporated into most decision support systems but are more limited in their representation of the geographic spread of diseases. Alternatively, CA as individual dynamic systems can capture the spatial and temporal pattern of the epidemics through local near-neighbor interactions. They consist of rather separate cells in one or multi-dimensional space, where each cell has a constant number of neighbors. Since CA can predict the geographic distribution of some epidemics through proper mathematical models, they have the potential of improving epidemic prediction and preventive measures in public health management. The results of the study show the improvement of the quality of epidemic response management through numerical modeling and epidemic spread studies using the ensemble simulation model based on random partition models and numerical analysis. |
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| ISSN: | 2455-7749 |